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Department of Neuroendocrinology, University of Lübeck, Lübeck 23538, Germany
ABSTRACT
Of late, an increasing number of studies have shown a strong relationship between sleep and memory. Here we summarize a series of our own studies in humans supporting a beneficial influence of slow-wave sleep (SWS) on declarative memory formation, and try to identify some mechanisms that might underlie this influence. Specifically, these experiments show that declarative memory benefits mainly from sleep periods dominated by SWS, whereas there is no consistent benefit of this memory from periods rich in rapid eye movement (REM) sleep. A main mechanism of declarative memory formation is believed to be the reactivation of newly acquired memory representations in hippocampal networks that stimulates a transfer and integration of these representations into neocortical neuronal networks. Consistent with this model, spindle activity and slow oscillation-related EEG coherence increase during early sleep after intense declarative learning in humans, signs that together point toward a neocortical reprocessing of the learned material. In addition, sleep seems to provide an optimal milieu for declarative memory reprocessing and consolidation by reducing cholinergic activation and the cortisol feedback to the hippocampus during SWS.
; Van Ormer
1933; Newman
1939). In recent years procedural memory consolidation has also
been found to benefit from sleep. For example, learning of a visual
discrimination task or speed in specific sequences of finger tapping were
found to be dependent on posttraining sleep intervals
(Karni et al. 1994;
Fischer et al. 2002;
Walker et al. 2002). In fact,
certain types of procedural memory develop only if subjects sleep after
training (Gais et al. 2000;
Stickgold et al. 2000a). Here
we concentrate on a number of studies conducted in the authors' laboratory to
enlighten some of the mechanisms underlying the enhancing effect of sleep on
declarative memory. The studies were all performed in humans, although they
rely on concepts derived from observations in animals. Different sleep stages for memory formation
After the discovery of rapid eye movement (REM) sleep by Aserinsky and
Kleitman (1953), most studies
were concerned with finding associations between sleep stages and memory
consolidation. The prevailing hypothesis—influenced by psychoanalytic
theory—was that new memory content would be reprocessed during dreaming,
which was thought to occur during REM sleep
(Feldman and Dement 1968;
Empson and Clarke 1970;
Chernik 1972;
Lewin and Glaubman 1975).
However, these studies, which all used declarative memory tasks, provided very
mixed results and were criticized for methodological reasons, leading some
researchers to reject a REM sleep-memory hypothesis completely
(Vertes and Eastman 2000;
Siegel 2001). The method used
in these experiments, namely, REM sleep deprivation, was particularly strongly
criticized for its nonspecific impairment of cognitive function
(Born and Gais 2000).
An experimental design preventing these unspecific effects of REM sleep
deprivation was devised by Ekstrand and colleagues
(Yaroush et al. 1971). They
had subjects learn a paired-associate list of words before the first or the
second half of the night. Then subjects slept for 3 h, and recall was tested
after sleep. Because the first half of the night contains high amounts of
slow-wave sleep (SWS) and the second half contains high amounts of REM sleep,
they were able to compare sleep with different percentages of these sleep
stages without disturbing the subjects' sleep. The amounts of non-REM sleep in
stages 1 and 2 in the retention sleep periods typically do not differ between
the first and second night half in this design. The results pointed for the
first time to an importance of SWS for declarative memory
(Yaroush et al. 1971;
Fowler et al. 1973). The same
design that splits nocturnal retention sleep into two halves was built upon in
two studies that used procedural memory tasks in addition to declarative tasks
(Plihal and Born 1997,
1999a). These studies found a
distinct positive influence of the SWS-rich first half of the night on
declarative memory consolidation in a paired associate wordlist and a spatial
learning task. Sleep from the second half of the night, which is rich in REM
sleep, improved nondeclarative memory tasks such as the procedural mirror
tracing task and priming processes.
This picture has become more and more complex over the past years with more
and more studies examining different tasks and different memory systems. For
example, consolidation of declarative memory for highly emotional material was
found to benefit additionally from sleep with high amounts of REM sleep
(Wagner et al. 2001). This and
related studies (Wagner et al.
2002) have led to the suggestion that REM sleep enhances emotional
memories. Other studies have indicated that, apart from SWS and REM sleep,
transitory sleep stages and particularly the human non-REM sleep stage 2 per
se contribute to memory formation, for example, in a simple procedural pursuit
rotor task and in cognitive procedural tasks
(Smith and MacNeill 1994;
Smith and Fazekas 1997). In
addition, procedural visual discrimination learning not only depends on REM
sleep but also requires a certain amount of preceding SWS
(Gais et al. 2000;
Stickgold et al. 2000b).
Together, these results provide good evidence that there are processes of memory consolidation taking place during sleep. However, although declarative memory benefits preferentially from periods in which SWS is predominant and the benefit for nondeclarative types of memory appears to be more related to sleep periods rich in REM sleep, the relationship between individual sleep stages and memory consolidation is probably not that simple. It is more likely that there are different neurophysiological processes at work during the various sleep stages underlying these consolidation phenomena, and that not all of these processes are involved in every form of memory. There are several candidate mechanisms that could account for the changes in declarative memory observed across sleep. These are mainly electrophysiological-, neurotransmitter-, and neuroendocrine-related mechanisms that are known to play a part in memory function and to exhibit different activity between SWS and REM sleep as well as between sleep and wakefulness.
Reactivation of newly formed memories during sleep
The covert reactivation of neuronal populations used for encoding the
respective materials during prior learning is thought to be a central
mechanism for memory consolidation during sleep
(Maquet 2001;
Stickgold et al. 2001;
McNaughton et al. 2003). The
reactivation during sleep remains covert because it is not associated with the
same or similar subjective experience or overt behaviors as the original
activation, suggesting that the underlying neuronal pattern of reactivation
also differs in some aspects from the original activation. Evidence for an
off-line reactivation has been provided mainly from studies in rats using
hippocampus-dependent spatial learning tasks. By using single and multiple
unit recordings, it was consistently found that hippocampal activity observed
during encoding was replayed during subsequent periods of SWS
(Pavlides and Winson 1989;
Wilson and McNaughton 1994;
Kudrimoti et al. 1999). One
study reported a hippocampal replay activity occurring during REM sleep after
learning (Louie and Wilson
2001). The replay activity during SWS does not appear to be
limited to the hippocampus but extends to other structures, including the
thalamus and neocortex (Qin et al.
1997; Ribeiro et al.
2004). In humans, it seems more difficult to reveal signs of a
replay of newly acquired memories during sleep. By using positron emission
tomography, Maquet et al.
(2000) demonstrated in healthy
humans a reactivation in the cuneus and the left premotor cortex during REM
sleep that followed training on a serial reaction time task. Because
performance on this task is thought not to depend on hippocampal function,
this finding fits well with the behavioral data discussed above, indicating
for hippocampus-independent procedural types of memory overall a greater
improvement from sleep periods rich in REM sleep. On the other hand,
reactivations observed in rats preferentially during SWS after training on a
hippocampus-dependent spatial task agree with our human data, indicating
preferential benefits of declarative memory from sleep periods rich in SWS.
However, whether and to what extent the signs of reactivation observed during
sleep play a functional role in consolidating respective neural memory
representations or merely reflect use-dependent phenomena of inert neural
activity is presently not clear.
With regard to declarative memory, it has been proposed that the
reactivation of hippocampal memory representations during sleep drives a
transfer of information to neocortical networks in which it becomes
consolidated and integrated into long-term representations residing in
neocortical networks (McClelland et al.
1995; Buzsáki
1996; McNaughton et al.
2003). According to this concept, during wakefulness new
information enters the hippocampal CA3 region through the entorhinal cortex,
where it is stored temporarily without disturbing previously acquired
memories. During non-REM and SWS, the flow of information is reversed and
hippocampal efferents to the neocortex become predominant. In line with this
concept, Qin et al. (1997)
found signs of coherent neuronal reactivation between hippocampal and
neocortical regions and within these regions during SWS following acquisition
of a spatial task in rats. Moreover, in the hippocampus the replay of activity
during SWS is linked to a sharp wave-ripple pattern of EEG activity. Notably,
the sharp wave-ripple pattern has been found to occur in close temporal
correlation with sleep oscillatory spindle activity (12-15 Hz) in the
neocortex, suggesting that spindle activity might be another indicator of
hippocampo-neocortical information transfer
(Siapas and Wilson 1998;
Sirota et al. 2003).
Neocortical spindle activity has been considered to be associated with massive
calcium influx into pyramidal cells that would via the activation of
calcium-sensitive kinases such as CaMKII predispose the cells to the induction
of plastic synaptic changes underlying long-term storage
(Contreras et al. 1997a;
Sejnowski and Destexhe
2000).
Electrophysiological signs: Spindle activity, direct current potentials, and slow oscillations
Against the background of the outlined reactivation concept for
sleep-dependent memory consolidation, the influence of intense learning of a
declarative task on the expression of spindles during subsequent intense
learning of a hippocampus-dependent declarative memory task
(Gais et al. 2002) was studied
in healthy humans. In the learning condition, subjects had to learn a list of
336 pairs of unrelated words in the 1-h period before they went to sleep. In
the nonlearning control condition, subjects were presented with a similar list
of word-pairs. However, this time they were required to count all letters
containing curved lines (e.g., J, P, U, but not W, Y, K) in the word displays.
Rated task difficulty was comparable for both conditions, but only in the
learning condition did subjects semantically process the words for explicit
learning. Although the structure of subsequent sleep architecture was
unchanged, there was a highly significant increase in the spindle density
following the learning condition compared with nonlearning
(Fig. 1). This was particularly
pronounced during the early part of sleep, averaging 33.5% in the first 90 min
after sleep onset. Additionally, sleep spindle density was correlated
positively with recall after sleep and with immediate recall tested during
learning before sleep. These findings agree with observations by Meier-Koll et
al. (1999), who reported a
similar increase in spindles during sleep in humans following learning a maze
task. Notably, Fogel et al.
(2002) reported increased
sleep spindle density in humans after training on a procedural pursuit rotor
task. Increased spindle activity could thus represent a general sign of
learning-dependent processing during sleep.
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Declarative memory processing during this early period of sleep is probably
also related to electrophysiological phenomena other than spindle activity.
Thus, during passage into SWS in humans slow-wave rhythms, including classical
delta activity (1-4 Hz) and slow oscillations (<1 Hz), increase
(Fig. 2). During SWS, power in
these frequency bands is maintained at a high level and only gradually
decreases over time. Notably, these changes in slow oscillatory and spindle
power across the initial sleep cycles are paralleled by distinct changes in
the transcortical direct current (DC) potential, which shifts steeply toward
negativity over frontocortical sites during transition into SWS. During SWS,
this DC-potential negativity is maintained and only slightly decreases toward
the end of the period (Marshall et al.
1996,
1998). The time course of
DC-potential changes is strongly correlated (with average coefficients r >
0.80) with changes in spindle, delta, and slow oscillatory activity
(Marshall et al. 2003). This
high correlation suggests that the generation of DC-potential negativity
during SWS is closely linked with mechanisms controlling the emergence of
spindle and slow oscillatory activity in thalamo-cortical feedback loops. It
may reflect gradual changes in extracellular ionic concentration (e.g., due to
calcium influx in cortical neurons and glial cells) resulting from the
generation of spindle and slow-wave rhythms, or influences of brainstem
neuromodulating systems on cortical excitability, thereby controlling the
emergence of cortical spindle and slow-wave rhythms
(Contreras et al. 1997b;
Massimini and Amzica 2001;
Amzica et al. 2002).
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Although the exact source of the DC-potential negativity during SWS is presently obscure, there are now hints that it contributes to declarative memory processing during human sleep (L. Marshall, M. Mölle, M. Hallschmid, and J. Born, in prep.). That study investigated the effects of transcranial DC stimulation (tDCS) on memory formation during periods of early SWS-rich sleep (Fig. 3). During sleep after a learning period, anodal tDCS was applied. This induces widespread extracellular DC-negativity in the neocortex and was therefore expected to add to the endogenous negative DC-potential during this period. Stimulation was applied at frontocortical electrode sites intermittently over a 30-min period beginning with the onset of the first period of SWS. Compared with placebo stimulation, anodal tDCS during SWS-rich sleep distinctly increased the retention of word-pairs. This improvement is remarkable because it was found in healthy young students who were already performing at a high level on the memory task, and it was found after a SWS-rich early sleep, which per se optimizes declarative memory. tDCS did not affect procedural memory and was also ineffective if applied during a wake retention period. Notably, when applied during SWS-rich sleep, anodal tDCS increased slow oscillatory activity, suggesting that the enhancing effect of tDCS on declarative memory involves enhanced slow oscillatory EEG activity.
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Slow oscillations have been associated with iterative processes of memory
formation during SWS in recent concepts based largely on cortical recordings
in animals (Sejnowski and Destexhe
2000; Steriade and Timofeev
2003). The slow oscillation can also be identified in the human
sleep EEG, in which a spectral peak in power at 
0.8 Hz is found
(Steriade et al. 1993;
Achermann and Borbély
1997; Marshall et al.
2000; Mölle et al.
2002). Slow oscillations grasp the entire thalamo-cortical system.
However, they can also be recorded in isolated slabs of neocortical tissue,
which for this reason is considered as their primary generator. In humans, the
negative half-wave of the slow oscillation corresponds to a depth-positive
neocortical field potential that is associated with widespread intracellular
hyperpolarization (Steriade
1994; Mölle et al.
2002). Conversely, the positive half-wave marks a depth-negative
extracellular field potential that reflects widespread cortical
depolarization. Slow oscillations via corticothalamic fibers therefore exert a
fundamental temporal grouping effect on spindle activity that is known to
originate from the thalamic nucleus reticularis
(Contreras and Steriade 1995;
Destexhe et al. 1999). Spindle
activity is stimulated during the depolarizing "up" state and
suppressed during the hyperpolarizing "down" state of the slow
oscillation. However, the grouping effect of slow oscillations also extends to
higher frequency bands, including ß and 
activity
(Mölle et al. 2002).
There is preliminary evidence that the grouping influence of slow
oscillations is also of relevance for the reprocessing of memories in humans
(M. Mölle, L. Marshall, S. Gais, and J. Born, in prep.). This study used
the coherence in scalp-recorded EEG activity as a correlate of the encoding of
memory representations. EEG coherence is a large-scale measure that depicts
the covariation in electrical activity between two distant brain regions. The
synchronized activity in distributed neocortical networks, as reflected by
coherence in EEG activity, can be considered to reflect the binding of
different aspects of a stimulus into a uniform representation. With the
encoding of complex representations as induced by written words and pictures,
involving associations between different modalities, binding extends over
distant brain regions and leads to increased coherence in different EEG
frequency bands between distant brain regions
(von Stein et al. 1999;
Weiss and Rappelsberger 2000).
In line with those studies, M. Mölle, L. Marshall, S. Gais, and J. Born
(in prep.) observed a marked increase in EEG coherence when subjects before a
period of nocturnal sleep learned a list of paired-associate words, compared
with a nonlearning control condition (in which subjects were required to count
the number of letters in words containing curved lines). This was reflected by
the fact that the number of electrode sites between which EEG coherence was
significantly higher during learning of the words than during nonlearning
drastically exceeded the number of electrode sites with a reversed relation.
During the early part of nocturnal sleep, there were only marginal increases
in EEG coherence after learning compared with nonlearning when analysis was
performed on periods of non-REM sleep in general. However, EEG coherence after
learning was strikingly increased when the analysis was performed time-locked
to the occurrence of slow oscillations during non-REM sleep and SWS. This
analysis revealed robust learning-dependent increases in coherence that
emerged selectively during the depolarizing up phase of the slow oscillations
and that were related to the delta and slow oscillatory as well as the spindle
and frequency bands. Although the learning task in this study was
complex and not designed to induce a specific topographical distribution of
coherences, this observation may be taken as an initial hint of a cortical
reprocessing of recently encoded memories in humans and entails a special
relevance for the grouping effect of slow oscillations.
In summary, the investigation of electrophysiological brain activity in
humans revealed increased spindle density during early non-REM sleep and in
EEG coherence during the depolarizing up state of slow oscillations after
declarative learning. Because these changes followed declarative learning, a
causative role of these phenomena for sleep-dependent memory consolidation
remains to be established. Nevertheless, such findings agree well with recent
concepts assuming that the consolidation of newly acquired declarative
memories and their integration for long-term storage relies on an iterative
reprocessing of these memories in hippocampo-neocortical circuitry
(Buzsáki 1998;
Sejnowski and Destexhe 2000;
Steriade and Timofeev 2003).
Central to this view is the grouping effect of slow oscillations originating
in the neocortex. The long-range synchrony in cortical activity associated
with the up state of slow oscillations drives thalamo-cortical spindle
activity, which through the strong and simultaneous calcium influx into
cortical pyramidal cells predisposes these cells toward the induction of
plastic synaptic changes that underlie long-term memory formation.
Concurrently, the strong synchronous cortical excitation associated with the
slow oscillation up state might facilitate the occurrence of hippocampal sharp
wave-ripple activity, a pattern thought to reflect reactivation of memories at
the hippocampal level and their propagation to neocortical networks
(Sirota et al. 2003). Slow
oscillations driving coincident inputs from thalamic spindle and hippocampal
sharp wave-ripple activity to cortical populations might in this way set the
stage for plastic processes specifically in neocortical representations of
newly encoded memories. Interestingly, miniature EPSP have been considered as
one mechanism initiating the depolarizing up state of slow oscillations. These
miniature potentials summate during the down state and thereby contribute to
the depolarization of cortical pyramidal cells above threshold
(Bazhenov et al. 2002). There
is some evidence suggesting that the probability of such miniature EPSPs
during the silent down phase is selectively enhanced at synapses previously
activated during associative learning
(Eliot et al. 1994;
Oliet et al. 1996;
Bao et al. 1998). If so, this
would mean that slow oscillation-driven reprocessing during sleep originates
preferentially in those neocortical neuron populations previously engaged in
encoding during the wake phase.
Acetylcholine, a neurotransmitter regulating sleep and memory
Acetylcholine (ACh) is a neurotransmitter that has long received much
attention in memory research, mainly in relation to Alzheimer's disease. It is
also involved in the regulation of the non-REM/REM sleep cycle
(Hobson et al. 1998).
Cholinergic activation in the central nervous system mainly stems from two
regions: the mesopontine tegmentum and the nucleus basalis of Meynert. The
cells of the pedunculopontine and laterodorsal tegmentum are genuinely
involved in the regulation of sleep stages, which is described in the
reciprocal-interaction model of REM/non-REM alternation
(Pace-Schott and Hobson 2002).
During wakefulness and REM sleep, these cells provide cholinergic input to
thalamocortical neurons, which in turn activate the cortex via glutamatergic
projections (Steriade 2003).
Other projections from the tegmentum, also using glutamate, activate the
nucleus basalis of Meynert, which in turn provides cholinergic activation
throughout the cortex (Rasmusson et al.
1994).
The effects of ACh on memory have to be regarded separately for the
acquisition, consolidation, and recall phase and for different memory systems
(Olton et al. 1991). A number
of studies did this by using either cholinergic receptor antagonists (e.g.,
scopolamine) or cholinesterase inhibitors (e.g., physostigmine). The latter
increase the availability of ACh by preventing its breakdown in the synaptic
cleft. The main outcome of these studies was a reduced acquisition of new
memories under conditions of cholinergic deficiency, perhaps via an influence
on attentional processes (Petersen
1977; Sarter et al.
2003).
According to a model of cholinergic memory modulation by Hasselmo
(1999), ACh inhibits feedback
loops within the hippocampus and between the hippocampus and neocortex. Thus,
high cholinergic activity during wakefulness allows encoding of new
declarative memories, whereas low cholinergic activity during SWS supports the
spontaneous replay of newly acquired information in the hippocampus. As
outlined above, this replay is thought to lead to a transfer of information
from the temporary hippocampal to the permanent neocortical storage and to
memory consolidation (Buzsáki
1989; Hasselmo
1999). Only very few studies have used declarative memory tasks
and postlearning modulation of the cholinergic system to directly test this
model. In one experiment, Rogers and Kesner
(2003) had rats learn a
hippocampus-dependent maze task. Before or after learning, they injected the
cholinesterase inhibitor physostigmine into the hippocampal CA3 region. They
conclude that "physostigmine impairs acquisition by a disruption of the
consolidation process."
In a study in our own laboratory, we used a similar experimental design in
humans to elucidate the role of ACh in sleep-dependent consolidation
(Gais and Born 2004). Subjects
learned paired-associate wordlists and practiced a mirror tracing task in the
evening before a 3-h period of sleep or wakefulness during which they received
an infusion of either physostigmine or placebo. Recall performance was then
tested. As hypothesized, the results showed significantly impaired wordlist
recall after physostigmine during sleep, whereas mirror tracing performance
was not affected. Thus, the predictions of Hasselmo's
(1999) model could be
confirmed (Fig. 4). The
inhibition of cholinergic activity during SWS plays an important role in
sleep-related memory consolidation, possibly by allowing feedback loops in the
hippocampus to become active. Notably, the effect of physostigmine did not
depend on changes of SWS or spindle activity because it occurred also in
subjects whose sleep architecture did not show obvious changes during infusion
of the cholinergic agonist. Because this oscillatory activity during SWS
arises from thalamocortical networks, this finding further supports the view
that physostigmine acted mainly on hippocampal memory representations in this
study.
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Neuroendocrine contributions to memory consolidation
The neuroendocrine hypothalamic-pituitary-adrenocortical system is related
to both memory formation and sleep. Cortisol (corticosterone in rats) released
from the adrenal gland is the main effector of this neuroendocrine system,
feeding back also to the brain. It enhances the acquisition of new and
interferes with retrieval of old memories during wakefulness
(de Quervain et al. 2000;
Roozendaal 2000;
Wolf, 2003). During the first
hours of nocturnal sleep, cortisol levels drop to a minimum due to circadian
rhythm and a synergistic inhibition of pituitary-adrenocortical activity by
SWS (Born et al. 1988;
Bierwolf et al. 1997).
Concurrently, declarative memory consolidation is enhanced during SWS-rich
early sleep. If, during this period of sleep, hormone levels are temporarily
augmented by infusion of cortisol, the beneficial effect of sleep on memory
consolidation is eradicated (Plihal and
Born 1999b). Procedural memory for mirror tracing skills was not
influenced by cortisol. This effect seems to be mediated by glucocorticoid
receptors (Plihal et al.
1999). There is evidence that activation of glucocorticoid
receptors suppresses hippocampal output from CA1 neurons
(de Kloet et al. 1998). This
kind of effect could explain a detrimental influence of glucocorticoids on
hippocampal memory reprocessing and on the propagation of the respective
excitatory output to neocortical networks. Thus, one way in which sleep
enhances memory consolidation seems to be by providing an optimal endocrine
environment and preventing hormonal feedback signals from interfering with
hippocampal memory processing. Other hormonal systems, such as the
somatotropic axis with its release of growth hormone, also show distinct
changes in their secretory activity during sleep and might also contribute
toward memory consolidation, but remain yet to be studied.
Conclusion
Together, these experiments present evidence that sleep provides special
conditions enhancing declarative memory consolidation, which in turn allow
memory traces to be actively reprocessed and strengthened during sleep. This
reprocessing might also include a transfer of information temporarily stored
in the hippocampus to neocortical brain regions, and a stabilization or
enhancement of synaptic connections. However, a qualitative reprocessing of
information during sleep might also occur when during this process newly
acquired memories become integrated with long-term memories assumed to reside
in neocortical networks (McClelland et al.
1995). In a recent study, Wagner et al.
(2004) had subjects do a
simple task that had one obvious (and long) and one hidden (and much shorter)
solution. Thus, subjects finding the hidden solution could greatly shorten the
time needed to complete the task. Subjects were confronted with the task for a
short period in the evening before an interval of sleep or wakefulness. When
subjects were confronted with the task again in the morning, those of them
having slept were significantly better in finding the hidden rule than those
who had not. They were also significantly better than were subjects who slept
during the night but were not confronted with the task before, ruling out
effects of fatigue and sleepiness. A possible interpretation of these data is
that the representation of the task built before sleep is reprocessed during
sleep and "restructured" in a way that allows the new solution to
be found.
Regarding the ever more complex data on the association between sleep and memory, all monocausal models attributing effects to one mechanism are likely to fail, especially if they use descriptive concepts such as "REM sleep" that represent an accumulation of many different physiological processes occurring simultaneously. Here, we have roughly outlined some of the physiological mechanisms that may contribute to the enhancement of declarative memory in an off-line process taking place during human sleep. Of course, it is far from being well-established that these mechanisms play a causative role for declarative memory consolidation during sleep. However, it is precisely this question that should stimulate further testing in a way that also takes into account that not just one but several mechanisms during the different sleep states act in concert to consolidate memory.
FOOTNOTES
Article and publication are at http://www.learnmem.org/cgi/doi/10.1101/lm.80504.
1 E-mail born{at}kfg.mu-luebeck.de; fax 49-4515003640.
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